A Review on Community Detection Algorithms in Social Networks for COVID-19 Related Fake News Detection, Management and Service Recommendation.

Ranjith K.¹ , Dhiya K.K.²

Section:Review Paper, Product Type: Journal-Paper
Vol.9 , Issue.5 , pp.30-37, Oct-2021

Online published on Oct 31, 2021

Copyright © Ranjith K., Dhiya K.K. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Social networks play a major role in information sharing than traditional information spreading since most people are active in social networking sites. We know, social platforms bring families and friends together regardless of their location. Community detection in social networks is very useful in offering customized services across communities. Though social platforms are useful in information sharing, it has been misused for fake news spreading as well. Studies show there is a steep rise in COVID-19 related misinformation spread through the internet community during the pandemic which affect people seriously. Therefore, an efficient technique is required to detect and manage this fake news and direct people by providing reliable news and services. In this paper, we review popular community analysis algorithms to build communities based on the user’s age and infection status to deliver reliable news, thereby preventing adverse effects of spreading COVID-19 related misinformation. Based on our observation, we propose a four-step process to validate the authenticity of the information shared on social media. In addition to this, the proposed system is extended to offer other reliable services to communities such as customized shopping recommendations, music recommendations, etc to each community based on participants’ behaviour. This would be helpful for them during lockdown, quarantine or isolation.

Key-Words / Index Term :
Community; Covid-19; Social networks; Fake news; Recommendations

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